Small docs improvements (#4041)

* Remove mention of run templates

* Add Jax and MLX materializer

* Improve wording on step.configure(...) usage

* Add missing link to caching page

* Switch order of docs pages

* Reorder core concepts

Author: schustmi

docs/book/getting-started/core-concepts.md

@@ -136,39 +136,40 @@ An **Orchestrator** is a workhorse that coordinates all the steps to run in a pi
 
 ZenML comes with a default _local orchestrator_ designed to run on your local machine. This is useful, especially during the exploration phase of your project. You don't have to rent a cloud instance just to try out basic things.
 
+#### Artifact Store
+
+An **Artifact Store** is a component that houses all data that passes through the pipeline as inputs and outputs. Each artifact that gets stored in the artifact store is tracked and versioned and this allows for extremely useful features like data caching, which speeds up your workflows.
+
+Similar to the orchestrator, ZenML comes with a default _local artifact store_ designed to run on your local machine. This is useful, especially during the exploration phase of your project. You don't have to set up a cloud storage system to try out basic things.
+
 #### Deployer
 
 A **Deployer** is a stack component that manages the deployment of pipelines as long-running HTTP servers useful for online mode execution. Unlike orchestrators that execute pipelines in batch mode, deployers can create and manage persistent services that wrap your pipeline in a web application, usually containerized, allowing it to be invoked through HTTP requests.
 
 ZenML comes with a _Docker deployer_ that can run deployments on your local machine as Docker containers, making it easy to test and develop real-time pipeline endpoints before moving to production infrastructure.
 
-#### Pipeline Run
+#### Flavor
 
-A **Pipeline Run** is a record of a pipeline execution. When you run a pipeline using an orchestrator, a pipeline run is created tracking information about the execution such as the status, the artifacts and metadata produced by the pipeline and all its steps. When a pipeline is deployed for online mode execution, a pipeline run is similarly created for every HTTP request made to it.
+ZenML provides a dedicated base abstraction for each stack component type. These abstractions are used to develop solutions, called **Flavors**, tailored to specific use cases/tools. With ZenML installed, you get access to a variety of built-in and integrated Flavors for each component type, but users can also leverage the base abstractions to create their own custom flavors.
+
+#### Stack Switching
+
+When it comes to production-grade solutions, it is rarely enough to just run your workflow locally without including any cloud infrastructure.
+
+Thanks to the separation between the pipeline code and the stack in ZenML, you can easily switch your stack independently from your code. For instance, all it would take you to switch from an experimental local stack running on your machine to a remote stack that employs a full-fledged cloud infrastructure is a single CLI command.
 
 #### Pipeline Snapshot
 
 A **Pipeline Snapshot** is an immutable snapshot of your pipeline that includes the pipeline DAG, code, configuration, and container images. Snapshots can be run from the server or dashboard, and can also be [deployed](#deployment).
 
-#### Artifact Store
-
-An **Artifact Store** is a component that houses all data that passes through the pipeline as inputs and outputs. Each artifact that gets stored in the artifact store is tracked and versioned and this allows for extremely useful features like data caching, which speeds up your workflows.
+#### Pipeline Run
 
-Similar to the orchestrator, ZenML comes with a default _local artifact store_ designed to run on your local machine. This is useful, especially during the exploration phase of your project. You don't have to set up a cloud storage system to try out basic things.
+A **Pipeline Run** is a record of a pipeline execution. When you run a pipeline using an orchestrator, a pipeline run is created tracking information about the execution such as the status, the artifacts and metadata produced by the pipeline and all its steps. When a pipeline is deployed for online mode execution, a pipeline run is similarly created for every HTTP request made to it.
 
 #### Deployment
 
 A **Deployment** is a running instance of a pipeline deployed as an HTTP endpoint. When you deploy a pipeline using a deployer, it becomes a long-running service that can be invoked through REST API calls. Each HTTP request to a deployment triggers a new pipeline run, creating the same artifacts and metadata tracking as traditional batch pipeline executions. This enables real-time inference, interactive ML workflows, and seamless integration with web applications and external services.
 
-#### Flavor
-
-ZenML provides a dedicated base abstraction for each stack component type. These abstractions are used to develop solutions, called **Flavors**, tailored to specific use cases/tools. With ZenML installed, you get access to a variety of built-in and integrated Flavors for each component type, but users can also leverage the base abstractions to create their own custom flavors.
-
-#### Stack Switching
-
-When it comes to production-grade solutions, it is rarely enough to just run your workflow locally without including any cloud infrastructure.
-
-Thanks to the separation between the pipeline code and the stack in ZenML, you can easily switch your stack independently from your code. For instance, all it would take you to switch from an experimental local stack running on your machine to a remote stack that employs a full-fledged cloud infrastructure is a single CLI command.
 
 ## 3. Management
 

docs/book/getting-started/your-first-ai-pipeline.md

@@ -236,7 +236,7 @@ def doc_analyzer(
   ```bash
   zenml pipeline deploy pipelines.doc_analyzer.doc_analyzer --config my_config.yaml
   ```
-- **Automate triggering**: [Create a run template](https://docs.zenml.io/user-guides/tutorial/trigger-pipelines-from-external-systems) (ZenML Pro) and trigger via API/webhooks from your app.
+- **Automate triggering**: [Create a snapshot](https://docs.zenml.io/user-guides/tutorial/trigger-pipelines-from-external-systems) and trigger via API/webhooks from your app (ZenML Pro only).
 - **Future enhancement**: Add an evaluation pipeline to score outputs and render reports if you need automated quality checks.
 
 ### Extend it

docs/book/how-to/artifacts/materializers.md

@@ -33,7 +33,7 @@ ZenML also provides a CloudpickleMaterializer that can handle any object by savi
 
 When you install ZenML integrations, additional materializers become available:
 
-<table data-full-width="true"><thead><tr><th width="199.5">Integration</th><th width="271">Materializer</th><th width="390">Handled Data Types</th><th>Storage Format</th></tr></thead><tbody><tr><td>bentoml</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-bentoml.html#zenml.integrations.bentoml">BentoMaterializer</a></td><td><code>bentoml.Bento</code></td><td><code>.bento</code></td></tr><tr><td>deepchecks</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-deepchecks.html#zenml.integrations.deepchecks">DeepchecksResultMateriailzer</a></td><td><code>deepchecks.CheckResult</code>, <code>deepchecks.SuiteResult</code></td><td><code>.json</code></td></tr><tr><td>evidently</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-evidently.html#zenml.integrations.evidently">EvidentlyProfileMaterializer</a></td><td><code>evidently.Profile</code></td><td><code>.json</code></td></tr><tr><td>great_expectations</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-great_expectations.html#zenml.integrations.great_expectations">GreatExpectationsMaterializer</a></td><td><code>great_expectations.ExpectationSuite</code>, <code>great_expectations.CheckpointResult</code></td><td><code>.json</code></td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFDatasetMaterializer</a></td><td><code>datasets.Dataset</code>, <code>datasets.DatasetDict</code></td><td>Directory</td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFPTModelMaterializer</a></td><td><code>transformers.PreTrainedModel</code></td><td>Directory</td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFTFModelMaterializer</a></td><td><code>transformers.TFPreTrainedModel</code></td><td>Directory</td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFTokenizerMaterializer</a></td><td><code>transformers.PreTrainedTokenizerBase</code></td><td>Directory</td></tr><tr><td>lightgbm</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-lightgbm.html#zenml.integrations.lightgbm">LightGBMBoosterMaterializer</a></td><td><code>lgbm.Booster</code></td><td><code>.txt</code></td></tr><tr><td>lightgbm</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-lightgbm.html#zenml.integrations.lightgbm">LightGBMDatasetMaterializer</a></td><td><code>lgbm.Dataset</code></td><td><code>.binary</code></td></tr><tr><td>neural_prophet</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-neural_prophet.html#zenml.integrations.neural_prophet">NeuralProphetMaterializer</a></td><td><code>NeuralProphet</code></td><td><code>.pt</code></td></tr><tr><td>pillow</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pillow.html#zenml.integrations.pillow">PillowImageMaterializer</a></td><td><code>Pillow.Image</code></td><td><code>.PNG</code></td></tr><tr><td>polars</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-polars.html#zenml.integrations.polars">PolarsMaterializer</a></td><td><code>pl.DataFrame</code>, <code>pl.Series</code></td><td><code>.parquet</code></td></tr><tr><td>pycaret</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pycaret.html#zenml.integrations.pycaret">PyCaretMaterializer</a></td><td>Any <code>sklearn</code>, <code>xgboost</code>, <code>lightgbm</code> or <code>catboost</code> model</td><td><code>.pkl</code></td></tr><tr><td>pytorch</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pytorch.html#zenml.integrations.pytorch">PyTorchDataLoaderMaterializer</a></td><td><code>torch.Dataset</code>, <code>torch.DataLoader</code></td><td><code>.pt</code></td></tr><tr><td>pytorch</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pytorch.html#zenml.integrations.pytorch">PyTorchModuleMaterializer</a></td><td><code>torch.Module</code></td><td><code>.pt</code></td></tr><tr><td>scipy</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-scipy.html#zenml.integrations.scipy">SparseMaterializer</a></td><td><code>scipy.spmatrix</code></td><td><code>.npz</code></td></tr><tr><td>spark</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-spark.html#zenml.integrations.spark">SparkDataFrameMaterializer</a></td><td><code>pyspark.DataFrame</code></td><td><code>.parquet</code></td></tr><tr><td>spark</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-spark.html#zenml.integrations.spark">SparkModelMaterializer</a></td><td><code>pyspark.Transformer</code></td><td><code>pyspark.Estimator</code></td></tr><tr><td>tensorflow</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-tensorflow.html#zenml.integrations.tensorflow">KerasMaterializer</a></td><td><code>tf.keras.Model</code></td><td>Directory</td></tr><tr><td>tensorflow</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-tensorflow.html#zenml.integrations.tensorflow">TensorflowDatasetMaterializer</a></td><td><code>tf.Dataset</code></td><td>Directory</td></tr><tr><td>whylogs</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-whylogs.html#zenml.integrations.whylogs">WhylogsMaterializer</a></td><td><code>whylogs.DatasetProfileView</code></td><td><code>.pb</code></td></tr><tr><td>xgboost</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-xgboost.html#zenml.integrations.xgboost">XgboostBoosterMaterializer</a></td><td><code>xgb.Booster</code></td><td><code>.json</code></td></tr><tr><td>xgboost</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-xgboost.html#zenml.integrations.xgboost">XgboostDMatrixMaterializer</a></td><td><code>xgb.DMatrix</code></td><td><code>.binary</code></td></tr></tbody></table>
+<table data-full-width="true"><thead><tr><th width="199.5">Integration</th><th width="271">Materializer</th><th width="390">Handled Data Types</th><th>Storage Format</th></tr></thead><tbody><tr><td>bentoml</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-bentoml.html#zenml.integrations.bentoml">BentoMaterializer</a></td><td><code>bentoml.Bento</code></td><td><code>.bento</code></td></tr><tr><td>deepchecks</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-deepchecks.html#zenml.integrations.deepchecks">DeepchecksResultMateriailzer</a></td><td><code>deepchecks.CheckResult</code>, <code>deepchecks.SuiteResult</code></td><td><code>.json</code></td></tr><tr><td>evidently</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-evidently.html#zenml.integrations.evidently">EvidentlyProfileMaterializer</a></td><td><code>evidently.Profile</code></td><td><code>.json</code></td></tr><tr><td>great_expectations</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-great_expectations.html#zenml.integrations.great_expectations">GreatExpectationsMaterializer</a></td><td><code>great_expectations.ExpectationSuite</code>, <code>great_expectations.CheckpointResult</code></td><td><code>.json</code></td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFDatasetMaterializer</a></td><td><code>datasets.Dataset</code>, <code>datasets.DatasetDict</code></td><td>Directory</td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFPTModelMaterializer</a></td><td><code>transformers.PreTrainedModel</code></td><td>Directory</td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFTFModelMaterializer</a></td><td><code>transformers.TFPreTrainedModel</code></td><td>Directory</td></tr><tr><td>huggingface</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-huggingface.html#zenml.integrations.huggingface">HFTokenizerMaterializer</a></td><td><code>transformers.PreTrainedTokenizerBase</code></td><td>Directory</td></tr><tr><td>lightgbm</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-lightgbm.html#zenml.integrations.lightgbm">LightGBMBoosterMaterializer</a></td><td><code>lgbm.Booster</code></td><td><code>.txt</code></td></tr><tr><td>lightgbm</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-lightgbm.html#zenml.integrations.lightgbm">LightGBMDatasetMaterializer</a></td><td><code>lgbm.Dataset</code></td><td><code>.binary</code></td></tr><tr><td>neural_prophet</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-neural_prophet.html#zenml.integrations.neural_prophet">NeuralProphetMaterializer</a></td><td><code>NeuralProphet</code></td><td><code>.pt</code></td></tr><tr><td>pillow</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pillow.html#zenml.integrations.pillow">PillowImageMaterializer</a></td><td><code>Pillow.Image</code></td><td><code>.PNG</code></td></tr><tr><td>polars</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-polars.html#zenml.integrations.polars">PolarsMaterializer</a></td><td><code>pl.DataFrame</code>, <code>pl.Series</code></td><td><code>.parquet</code></td></tr><tr><td>pycaret</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pycaret.html#zenml.integrations.pycaret">PyCaretMaterializer</a></td><td>Any <code>sklearn</code>, <code>xgboost</code>, <code>lightgbm</code> or <code>catboost</code> model</td><td><code>.pkl</code></td></tr><tr><td>pytorch</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pytorch.html#zenml.integrations.pytorch">PyTorchDataLoaderMaterializer</a></td><td><code>torch.Dataset</code>, <code>torch.DataLoader</code></td><td><code>.pt</code></td></tr><tr><td>pytorch</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-pytorch.html#zenml.integrations.pytorch">PyTorchModuleMaterializer</a></td><td><code>torch.Module</code></td><td><code>.pt</code></td></tr><tr><td>scipy</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-scipy.html#zenml.integrations.scipy">SparseMaterializer</a></td><td><code>scipy.spmatrix</code></td><td><code>.npz</code></td></tr><tr><td>spark</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-spark.html#zenml.integrations.spark">SparkDataFrameMaterializer</a></td><td><code>pyspark.DataFrame</code></td><td><code>.parquet</code></td></tr><tr><td>spark</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-spark.html#zenml.integrations.spark">SparkModelMaterializer</a></td><td><code>pyspark.Transformer</code></td><td><code>pyspark.Estimator</code></td></tr><tr><td>tensorflow</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-tensorflow.html#zenml.integrations.tensorflow">KerasMaterializer</a></td><td><code>tf.keras.Model</code></td><td>Directory</td></tr><tr><td>tensorflow</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-tensorflow.html#zenml.integrations.tensorflow">TensorflowDatasetMaterializer</a></td><td><code>tf.Dataset</code></td><td>Directory</td></tr><tr><td>whylogs</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-whylogs.html#zenml.integrations.whylogs">WhylogsMaterializer</a></td><td><code>whylogs.DatasetProfileView</code></td><td><code>.pb</code></td></tr><tr><td>xgboost</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-xgboost.html#zenml.integrations.xgboost">XgboostBoosterMaterializer</a></td><td><code>xgb.Booster</code></td><td><code>.json</code></td></tr><tr><td>xgboost</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-xgboost.html#zenml.integrations.xgboost">XgboostDMatrixMaterializer</a></td><td><code>xgb.DMatrix</code></td><td><code>.binary</code></td></tr><tr><td>jax</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-jax.html#zenml.integrations.jax">JAXArrayMaterializer</a></td><td><code>jax.Array</code></td><td><code>.npy</code></td></tr><tr><td>mlx</td><td><a href="https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-mlx.html#zenml.integrations.mlx">MLXArrayMaterializer</a></td><td><code>mlx.core.array</code></td><td><code>.npy</code></td></tr></tbody></table>
 
 > **Note**: When using Docker-based orchestrators, you must specify the appropriate integrations in your `DockerSettings` to ensure the materializers are available inside the container.